Can simple be useful and reliable? Using ecological indicators to represent and compare the states of marine ecosystems
Bibliographic record
Abstract
Abstract Shin, Y-J., Bundy, A., Shannon, L. J., Simier, M., Coll, M., Fulton, E. A., Link, J. S., Jouffre, D., Ojaveer, H., Mackinson, S., Heymans, J. J., and Raid, T. 2010. Can simple be useful and reliable? Using ecological indicators to represent and compare the states of marine ecosystems. – ICES Journal of Marine Science, 67: 717–731. Within the IndiSeas WG, the evaluation of exploited marine ecosystems has several steps, from simple binary categorization of ecosystems to a more-complex attempt to rank them and to evaluate their status using decision-tree analyses. With the intention of communicating scientific knowledge to the public and stakeholders, focus is on evaluating and comparing the status of exploited marine ecosystems using a set of six ecological indicators and a simple and transparent graphic representation of ecosystem state (pie charts). A question that arose was whether it was acceptable to compare different types of marine ecosystems using a generic set of indicators. To this end, an attempt is made to provide reference levels to which ecosystems can be objectively compared. Unacceptable thresholds for each indicator are determined based on ecological expertise derived from a questionnaire distributed to a group of scientific experts. Analysis of the questionnaires revealed no significant difference in the thresholds provided for different ecosystem types, suggesting that it was reasonable to compare states directly across different types of ecosystem using the set of indicators selected.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".